Computational tools for population-scale assembly and genotyping with pangenomes

The National Institutes of Health National Human Genome Research Institute awarded a $1.967 million Cooperative Agreement to Yale University on May 11, 2026, under the Human Genome Research program (CFDA 93.172) to develop computational tools for population-scale pangenome assembly and genotyping.

Yale will develop co-assembly pipelines that enable cost-efficient pangenome assembly from low-coverage sequencing data, create privacy-preserving tools for utilizing pangenomes with private or controlled-access data, and build production-grade software and resources to facilitate adoption by the genomics community. The work addresses challenges in scaling the Human Pangenome Reference Consortium's expansion to 350 individuals by reducing the cost of long-read sequencing and removing barriers to data sharing across institutions.

Performance occurs in New Haven, Connecticut through April 30, 2029. The Trustees of Indiana University, led by Professors Tang, receives a sub-award to develop efficient algorithms for pangenome-based genotyping of multiallelic genes and privacy-preserving methods for pangenome-based analysis of multiallelic genes. The Indiana University sub-award was dated June 24, 2026.

Abstract

Capturing the full allelic diversity in highly variable regions of the human genome requires large- scale sequencing efforts, yet current approaches face significant cost and privacy-related chal- lenges. The human pangenome reference consortium (hprc) aims to expand its pangenome reference to 350 individuals by 2025, but scaling further remains difficult due to the high cost of long-read sequencing and barriers to data sharing across institutions. To address these challenges, our proposal brings together experts in genome assembly, genotyping, and privacy technologies to develop scalable and privacy-preserving methods for pangenome construction and analysis. Our specific aims include: developing CO-assembly pipelines that leverage shared genetic variation to enable cost-efficient pangenome assembly from low-coverage sequencing data (aim 1), creating privacy-preserving tools for utilizing pangenomes together with private or controlled-access data (aim 2), and building production-grade software and resources to facilitate adoption of our tools by the genomics community (aim 3). By enabling efficient, secure, and scalable pangenome CON- struction, our work aims to accelerate population-scale pangenomics research and improve analysis of complex, multiallelic regions of the human genome.